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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:39122
- loss:MultipleNegativesRankingLoss
base_model: google/embeddinggemma-300m
widget:
- source_sentence: 组件即将出现时加载收藏商家数据
sentences:
- "static async delete(key: string, preferenceName: string = defaultPreferenceName)\
\ {\n let preferences = await this.getPreferences(preferenceName)\n return\
\ await preferences.delete(key)\n }"
- "async aboutToAppear(): Promise<void> {\n await this.loadFavoriteMerchants();\n\
\ }"
- 'Copyright (c) 2022 Huawei Device Co., Ltd.
Licensed under the Apache License,Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.'
- source_sentence: "@Builder\n buildBottomNavigation() {\n Tabs({ index: this.currentTabIndex\
\ }) {\n TabContent() {\n // 首页内容在主区域显示\n }\n .tabBar(this.buildTabBarItem('首页',\
\ $r('app.media.ic_home'), 0))\n \n TabContent() {\n // 联系人内容在主区域显示\n\
\ }\n .tabBar(this.buildTabBarItem('联系人', $r('app.media.ic_contacts'),\
\ 1))\n \n TabContent() {\n // 日历内容在主区域显示\n }\n .tabBar(this.buildTabBarItem('日历',\
\ $r('app.media.ic_calendar'), 2))\n \n TabContent() {\n // 祝福语内容在主区域显示\n\
\ }\n .tabBar(this.buildTabBarItem('祝福语', $r('app.media.ic_greetings'),\
\ 3))\n \n TabContent() {\n // 设置内容在主区域显示\n }\n .tabBar(this.buildTabBarItem('设置',\
\ $r('app.media.ic_settings'), 4))\n }\n .onChange((index: number) => {\n\
\ this.onTabChange(index);\n })\n .barPosition(BarPosition.End)\n \
\ .barBackgroundColor('#ffffff')\n .barHeight(60)\n }"
sentences:
- 定义List的builder方法
- 错误相关常量
- 构建底部导航栏
- source_sentence: 插入数据库
sentences:
- "static dateToTimestamp(date: Date): number {\n return date.getTime();\n }"
- "public async insertData(context: common.Context, Contact: Contact): Promise<void>\
\ {\n logger.info(TAG, 'insert begin');\n if (!context) {\n logger.info(TAG,\
\ 'context is null or undefined');\n }\n\n const predicates = new rdb.RdbPredicates(TABLE_NAME);\n\
\ if (predicates === null || predicates === undefined) {\n logger.info(TAG,\
\ 'predicates is null or undefined');\n }\n\n this.rdbStore = await rdb.getRdbStore(context,\
\ STORE_CONFIG);\n\n let value1 = Contact.name;\n let value2 = Contact.phone;\n\
\ let value3 = Contact.email;\n let value4 = Contact.address;\n let value5\
\ = Contact.avatar;\n let value6 = Contact.category;\n\n const valueBucket:\
\ ValuesBucket = {\n 'name': value1,\n 'phone': value2,\n 'email':\
\ value3,\n 'address': value4,\n 'avatar': value5,\n 'category':\
\ value6\n }\n\n if (this.rdbStore != undefined) {\n this.rdbStore.insert(TABLE_NAME,\
\ valueBucket, rdb.ConflictResolution.ON_CONFLICT_REPLACE,\n (err: BusinessError,\
\ rows: number) => {\n if (err) {\n logger.info(TAG, \"Insert\
\ failed, err: \" + err)\n return\n }\n logger.info(TAG,\
\ `insert done:${rows}`);\n promptAction.showToast({\n message:\
\ $r('app.string.operate_rdb_in_taskpool_add_prompt_text', Contact.name),\n \
\ duration: CommonConstants.PROMPT_DURATION_TIME\n });\n \
\ })\n }\n }"
- 日历日期的代办事项
- source_sentence: "private recordOperation(\n type: 'create' | 'update' | 'delete'\
\ | 'complete' | 'cancel',\n todoId: string,\n changes?: ChangeRecord,\n\
\ description?: string\n ): void {\n try {\n const record: TodoOperationRecord\
\ = {\n id: this.generateId(),\n type,\n todoId,\n \
\ changes,\n timestamp: new Date().toISOString(),\n description\n\
\ };\n\n this.operationRecords.unshift(record);\n \n // 只保留最近100条记录\n\
\ if (this.operationRecords.length > 100) {\n this.operationRecords\
\ = this.operationRecords.slice(0, 100);\n }\n\n hilog.info(LogConstants.DOMAIN_APP,\
\ LogConstants.TAG_APP, `Recorded operation: ${type} for todo ${todoId}`);\n \
\ } catch (error) {\n hilog.error(LogConstants.DOMAIN_APP, LogConstants.TAG_APP,\
\ `Failed to record operation: ${error}`);\n }\n }"
sentences:
- 记录操作
- "export interface DataConfig {\n autoBackup: AutoBackupConfig;\n dataRetention:\
\ DataRetentionConfig;\n syncConfig: SyncConfig;\n}"
- "@Builder\n ExamSwitchModule() {\n Row() {\n Text('切换题库：')\n .fontSize(14)\n\
\ Text( this.guideService.guideData.licenseType !== undefined?licenseTypeName[this.guideService.guideData.licenseType]:'')\n\
\ .fontSize(14)\n .fontColor('#64BB5C')\n Image($r('app.media.right_triangle'))\n\
\ .width(16)\n .height(16)\n .fillColor('rgba(0,0,0,0.9)')\n\
\ }\n .width('100%')\n .justifyContent(FlexAlign.Start)\n .onClick(()\
\ => {\n this.vm.navStack.pushPathByName('guidePage', true)\n })\n }"
- source_sentence: 'resize(size: number): void;'
sentences:
- "Resize the bitVector's length.\n\n@param { number } size - The new size for bitVector.\
\ If count is greater than the current size of bitVector,\nthe additional bit\
\ elements are set to 0.\n@throws { BusinessError } 401 - Parameter error. Possible\
\ causes:\n1.Mandatory parameters are left unspecified.\n2.Incorrect parameter\
\ types.\n@throws { BusinessError } 10200011 - The resize method cannot be bound.\n\
@throws { BusinessError } 10200201 - Concurrent modification error.\n@syscap SystemCapability.Utils.Lang\n\
@atomicservice\n@since 12\n \nResize the bitVector's length.\n\n@param { number\
\ } size - The new size for bitVector. If count is greater than the current size\
\ of bitVector,\nthe additional bit elements are set to 0.\n@throws { BusinessError\
\ } 401 - Parameter error. Possible causes:\n1.Mandatory parameters are left unspecified.\n\
2.Incorrect parameter types.\n@throws { BusinessError } 10200011 - The resize\
\ method cannot be bound.\n@throws { BusinessError } 10200201 - Concurrent modification\
\ error.\n@syscap SystemCapability.Utils.Lang\n@crossplatform\n@atomicservice\n\
@since 18"
- "makeNode(uiContext: UIContext): FrameNode {\n this.rootNode = new FrameNode(uiContext);\n\
\ if (this.rootNode !== null) {\n this.rootRenderNode = this.rootNode.getRenderNode();\n\
\ }\n return this.rootNode;\n }"
- "export interface OnlineLunarYear {\n year: number;\n zodiac: string;\n ganzhi:\
\ string;\n leapMonth: number;\n isLeapYear: boolean;\n leapMonthDays?: number;\n\
\ solarTerms: SolarTermInfo[];\n festivals: LunarFestival[];\n}"
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on google/embeddinggemma-300m
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 57c266a740f537b4dc058e1b0cda161fd15afa75 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
(3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
(4): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("hreyulog/embedinggemma_arkts")
# Run inference
queries = [
"Transform an array of points with all matrices. VERY IMPORTANT: Keep\nmatrix order \"value-touch-offset\" when transforming.\n\n@param pts",
]
documents = [
"public pointValuesToPixel(pts: number[]) {\n this.mMatrixValueToPx.mapPoints(pts);\n this.mViewPortHandler.getMatrixTouch().mapPoints(pts);\n this.mMatrixOffset.mapPoints(pts);\n }",
'makeNode(uiContext: UIContext): FrameNode {\n this.rootNode = new FrameNode(uiContext);\n if (this.rootNode !== null) {\n this.rootRenderNode = this.rootNode.getRenderNode();\n }\n return this.rootNode;\n }',
'export interface OnlineLunarYear {\n year: number;\n zodiac: string;\n ganzhi: string;\n leapMonth: number;\n isLeapYear: boolean;\n leapMonthDays?: number;\n solarTerms: SolarTermInfo[];\n festivals: LunarFestival[];\n}',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.8923, 0.0264, -0.0212]])
```
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### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Evaluation Results
On arkts-code-docstring dataset split test
| Model | Params | MRR | NDCG@5 | Recall@1 | Recall@5 |
|------|--------|-----|--------|----------|----------|
| **embedinggemma_arkts** | 308M | **0.7788** | **0.8034** | **0.7142** | **0.8769** |
| QWEN3-Embedding-0.6B | 596M | 0.6776 | 0.7015 | 0.6141 | 0.7723 |
| embeddinggemma-300m | 308M | 0.6399 | 0.6654 | 0.5740 | 0.7416 |
| BGE-M3 | 567M | 0.5283 | 0.5603 | 0.4464 | 0.6558 |
| BGE-base-zh-v1.5 | 110M | 0.3598 | 0.3903 | 0.2841 | 0.4816 |
| BGE-base-en-v1.5 | 110M | 0.3439 | 0.3637 | 0.2935 | 0.4227 |
| E5-base-v2 | 110M | 0.3073 | 0.3261 | 0.2596 | 0.3823 |
| BM25 (jieba) | – | 0.2043 | 0.2204 | 0.1643 | 0.2690 |
## Training Details
### Training Dataset
Dataset: [hreyulog/arkts-code-docstring](https://huggingface.co/datasets/hreyulog/arkts-code-docstring)
* Size: 39,122 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 97.17 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 94.4 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>移除登录状态监听</code> | <code>public removeLoginStateListener(listener: (isLoggedIn: boolean) => void) {\n const index = this.loginStateListeners.indexOf(listener);\n if (index !== -1) {\n this.loginStateListeners.splice(index, 1);\n }\n }</code> |
| <code>PUT请求</code> | <code>static put<T = Object>(url: string, data?: Object, config: RequestConfig = {}): Promise<HttpResponse<T>> {<br> const putConfig: RequestConfig = {<br> method: http.RequestMethod.PUT,<br> headers: config.headers,<br> timeout: config.timeout,<br> data: data<br> };<br> return HttpUtil.request<T>(url, putConfig);<br> }</code> |
| <code>Transform an array of points with all matrices. VERY IMPORTANT: Keep\nmatrix order \"value-touch-offset\" when transforming.\n\n@param pts</code> | <code>public pointValuesToPixel(pts: number[]) {\n this.mMatrixValueToPx.mapPoints(pts);\n this.mViewPortHandler.getMatrixTouch().mapPoints(pts);\n this.mMatrixOffset.mapPoints(pts);\n }</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 2
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: None
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `enable_jit_checkpoint`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `use_cpu`: False
- `seed`: 42
- `data_seed`: None
- `bf16`: False
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: -1
- `ddp_backend`: None
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `hub_revision`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `auto_find_batch_size`: False
- `full_determinism`: False
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `use_cache`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.4088 | 500 | 0.3798 |
| 0.8177 | 1000 | 0.2489 |
| 1.2265 | 1500 | 0.1308 |
| 1.6353 | 2000 | 0.0877 |
### Framework Versions
- Python: 3.10.19
- Sentence Transformers: 5.2.2
- Transformers: 5.0.0
- PyTorch: 2.9.1
- Accelerate: 1.12.0
- Datasets: 4.5.0
- Tokenizers: 0.22.2
## Citation
### BibTeX
#### ArkTS-CodeSearch
```bibtex
@misc{he2026arktscodesearchopensourcearktsdataset,
title={ArkTS-CodeSearch: A Open-Source ArkTS Dataset for Code Retrieval},
author={Yulong He and Artem Ermakov and Sergey Kovalchuk and Artem Aliev and Dmitry Shalymov},
year={2026},
eprint={2602.05550},
archivePrefix={arXiv},
primaryClass={cs.SE},
url={https://arxiv.org/abs/2602.05550},
}
```
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